The PSVM software contains a classification, a regression, and a feature selection mode and is based on an efficient SMO optimization technique. The software can directly be applied to dyadic (matrix) data sets or it can be used with kernels like standard SVM software. PSVM minimizes a scale-invariant capacity measure under a new set of constraints. As a result, and in contrast to standard SVMs, the kernel function does not have to be positive definite, the data matrices have not to be square, e.g. the software already implements the indefinite sin-kernel which is a non-Mercer kernel. Another important feature of the software is that is allows for n-fold cross validation and hyperparameter selection. For classification tasks it offers the determination of the significance level and ROC data. In summary the basic features of the software are